Anagha Kulkarni

About

Anagha Kulkarni is a prominent AI and robotics researcher whose work sits at the intersection of human-robot interaction, task planning, and interpretable artificial intelligence. Her research focuses on making autonomous agents more transparent and comprehensible to human collaborators — a challenge that grows increasingly critical as intelligent systems are deployed in safety-sensitive environments. Kulkarni's most influential contribution, "Plan Explicability and Predictability for Robot Task Planning" (2017, 135 citations), established foundational frameworks for ensuring that robot-generated plans align with human expectations, reducing cognitive load and improving safety. This work introduced the concept of explicable planning, which she further refined by framing it as minimizing the distance between an agent's behavior and what humans anticipate — a formulation that earned additional recognition across multiple publications. Her research extends into mixed-reality workspaces and alternative human-robot communication modes, including electrophysiological monitoring and augmented reality interfaces, demonstrating a remarkably broad methodological range. More recently, her 2021 Bayesian unification framework brought previously fragmented interpretability measures under a single coherent model, signaling her ambition to systematize the field. With over 270 cumulative citations, Kulkarni's contributions have meaningfully shaped how researchers think about trust, transparency, and collaboration between humans and autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Plan explicability and predictability for robot task planning

   135 citations · 2017
2. 2
   Projection-Aware Task Planning and Execution for Human-in-the-Loop Operation of Robots in a Mixed-Reality Workspace

   47 citations · 2018
3. 3
   Explicable Planning as Minimizing Distance from Expected Behavior

   29 citations · 2019
4. 4
   Alternative Modes of Interaction in Proximal Human-in-the-Loop Operation of Robots

   28 citations · 2017
5. 5
   Plan Explicability and Predictability for Robot Task Planning

   23 citations · 2015
6. 6
   Explicable Robot Planning as Minimizing Distance from Expected Behavior.

   20 citations · 2016
7. 7
   A Unifying Bayesian Formulation of Measures of Interpretability in Human-AI Interaction

   8 citations · 2021
8. 8
   Designing Environments Conducive to Interpretable Robot Behavior

   5 citations · 2020
9. 9
   Explaining in the Presence of Vocabulary Mismatch

   2 citations · 2022
10. 10
    Explicablility as Minimizing Distance from Expected Behavior

    2 citations · 2016

Key Collaborators

Contact & Links